Replaced deprecated numpy data types. Bumped version.

Signed-off-by: Daniel Scheffler <[email protected]>

HISTORY.rst

@@ -2,6 +2,12 @@
 History
 =======
 
+0.10.2 (2021-12-16)
+-------------------
+
+* Replaced deprecated numpy data types.
+
+
 0.10.1 (2021-12-15)
 -------------------
 

spechomo/classifier_creation.py

@@ -226,7 +226,7 @@ def generate_reference_cubes(self, fmt_out='ENVI', try_read_dumped_clf=True, sam
                 self.logger.info('Performing spectral resampling to match %s %s specifications...' % (tgt_sat, tgt_sen))
                 unif_random_spectra_rsp = self.resample_spectra(
                     unif_random_spectra,
-                    src_cwl=np.array(src_im.meta.band_meta['wavelength'], dtype=np.float).flatten(),
+                    src_cwl=np.array(src_im.meta.band_meta['wavelength'], dtype=float).flatten(),
                     tgt_rsr=self._get_tgt_RSR_object(tgt_sat, tgt_sen),
                     nodataVal=src_im.nodata,
                     alg_nodata=alg_nodata)
@@ -392,7 +392,7 @@ def resample_image_spectrally(self, src_im, tgt_rsr, src_nodata=None, alg_nodata
 
         # read input image
         self.logger.info('Reading the input image %s...' % im_name)
-        im_gA.cwl = np.array(im_gA.meta.band_meta['wavelength'], dtype=np.float).flatten()
+        im_gA.cwl = np.array(im_gA.meta.band_meta['wavelength'], dtype=float).flatten()
 
         # perform spectral resampling of input image to match spectral properties of target sensor
         self.logger.info('Performing spectral resampling to match spectral properties of %s %s...'
@@ -495,7 +495,7 @@ def mean_absolute_percentage_error(y_true, y_pred):
 
             # avoid division by 0
             if 0 in y_true:
-                y_true = y_true.astype(np.float)
+                y_true = y_true.astype(float)
                 y_true[y_true == 0] = np.nan
 
             return np.nanmean(np.abs((y_true - y_pred) / y_true), axis=0) * 100

spechomo/clustering.py

@@ -106,7 +106,7 @@ def goodSpecMask(self):
 
                 self._goodSpecMask = goodSpecMask
             else:
-                self._goodSpecMask = np.ones((self.im.rows * self.im.cols), dtype=np.bool)
+                self._goodSpecMask = np.ones((self.im.rows * self.im.cols), dtype=bool)
 
         return self._goodSpecMask
 
@@ -256,7 +256,7 @@ def compute_euclidian_distance_2D(spectra, endmembers):
 
         # loop over all endmember spectra and compute spectral angle for each input spectrum
         for n_sample in range(n_samples):
-            train_spectrum = endmembers[n_sample, :].astype(np.float)
+            train_spectrum = endmembers[n_sample, :].astype(float)
             diff = spectra - train_spectrum
             dists[:, n_sample] = np.sqrt((diff ** 2).sum(axis=1))
 
@@ -272,7 +272,7 @@ def compute_euclidian_distance_for_labelled_spectra(spectra, labels, endmembers)
 
         # loop over all endmember spectra and compute spectral angle for each input spectrum
         for lbl in np.unique(labels):
-            train_spectrum = endmembers[lbl, :].astype(np.float)
+            train_spectrum = endmembers[lbl, :].astype(float)
             mask_curlbl = labels == lbl
             spectra_curlbl = spectra[mask_curlbl, :]
             diff_curlbl = spectra_curlbl - train_spectrum
@@ -321,7 +321,7 @@ def spectral_distances_with_nodata(self):
             if self.n_spectra == (self.im.rows * self.im.cols):
                 self._spectral_distances_with_nodata = self.spectral_distances
             else:
-                dists = np.full_like(self.goodSpecMask, np.nan, dtype=np.float)
+                dists = np.full_like(self.goodSpecMask, np.nan, dtype=float)
                 dists[self.goodSpecMask] = self.spectral_distances
                 self._spectral_distances_with_nodata = dists
 
@@ -341,7 +341,7 @@ def spectral_angles_with_nodata(self):
             if self.n_spectra == (self.im.rows * self.im.cols):
                 self._spectral_angles_with_nodata = self.spectral_angles
             else:
-                angles = np.full_like(self.goodSpecMask, np.nan, dtype=np.float)
+                angles = np.full_like(self.goodSpecMask, np.nan, dtype=float)
                 angles[self.goodSpecMask] = self.spectral_angles
                 self._spectral_angles_with_nodata = angles
 

spechomo/resampling.py

@@ -56,7 +56,7 @@ def __init__(self, wvl_src, rsr_tgt, logger=None):
         self._wvl_1nm = None
         self._rsr_1nm = {}
 
-        wvl_src = np.array(wvl_src, dtype=np.float).flatten()
+        wvl_src = np.array(wvl_src, dtype=float).flatten()
         if rsr_tgt.wvl_unit != 'nanometers':
             rsr_tgt.convert_wvl_unit()
 
@@ -106,7 +106,7 @@ def resample_signature(self, spectrum, scale_factor=10000, nodataVal=None, alg_n
         if not spectrum.ndim == 1:
             raise ValueError("The array of the given spectral signature must be 1-dimensional. "
                              "Received a %s-dimensional array." % spectrum.ndim)
-        spectrum = np.array(spectrum, dtype=np.float).flatten()
+        spectrum = np.array(spectrum, dtype=float).flatten()
         assert spectrum.size == self.wvl_src_nm.size
 
         # convert spectrum to one multispectral image pixel and resample it
@@ -253,7 +253,7 @@ def _resample_tile_mp(tilebounds, nodataVal=None, alg_nodata='radical'):
         else:
             raise NotImplementedError(nodataVal)
 
-        tiledata = tiledata.astype(np.float)
+        tiledata = tiledata.astype(float)
         tiledata[_mask_bool3d] = np.nan
 
         # fill pixels with all bands nodata

spechomo/version.py

@@ -28,5 +28,5 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-__version__ = '0.10.1'
-__versionalias__ = '20211215.01'
+__version__ = '0.10.2'
+__versionalias__ = '20211216.01'

tests/test_classifier_creation.py

@@ -101,7 +101,7 @@ def test_resample_spectra(self):
         # tgt_rsr = RSR(satellite='Terra', sensor='MODIS', sort_by_cwl=True)
         unif_random_spectra_rsp = \
             self.SHC.resample_spectra(unif_random_spectra,
-                                      src_cwl=np.array(src_im.meta.band_meta['wavelength'], dtype=np.float).flatten(),
+                                      src_cwl=np.array(src_im.meta.band_meta['wavelength'], dtype=float).flatten(),
                                       tgt_rsr=tgt_rsr,
                                       nodataVal=src_im.nodata)
         self.assertIsInstance(unif_random_spectra_rsp, np.ndarray)

tests/test_resampling.py

@@ -65,7 +65,7 @@ def setUpClass(cls):
 
     def test_resample_signature(self):
         # Get a hyperspectral spectrum.
-        spectrum_wvl = np.array(self.geoArr.meta.band_meta['wavelength'], dtype=np.float).flatten()
+        spectrum_wvl = np.array(self.geoArr.meta.band_meta['wavelength'], dtype=float).flatten()
         spectrum = self.geoArr[0, 0, :].flatten()
 
         sr = SR(spectrum_wvl, self.rsr_l8)
@@ -74,7 +74,7 @@ def test_resample_signature(self):
 
     def test_resample_signature_with_nodata(self):
         # Get a hyperspectral spectrum.
-        spectrum_wvl = np.array(self.geoArr.meta.band_meta['wavelength'], dtype=np.float).flatten()
+        spectrum_wvl = np.array(self.geoArr.meta.band_meta['wavelength'], dtype=float).flatten()
         spectrum = self.geoArr[0, 0, :].flatten()
         spectrum[130: 140] = -9999
 
@@ -84,7 +84,7 @@ def test_resample_signature_with_nodata(self):
 
     def test_resample_image(self):
         # Get a hyperspectral image.
-        image_wvl = np.array(self.geoArr.meta.band_meta['wavelength'], dtype=np.float).flatten()
+        image_wvl = np.array(self.geoArr.meta.band_meta['wavelength'], dtype=float).flatten()
         image = self.geoArr[:]
 
         sr = SR(image_wvl, self.rsr_l8)
@@ -94,7 +94,7 @@ def test_resample_image(self):
     @unittest.SkipTest
     def test__resample_tile_with_nodata(self):
         # Get a hyperspectral image tile.
-        image_wvl = np.array(self.geoArr.meta.band_meta['wavelength'], dtype=np.float).flatten()
+        image_wvl = np.array(self.geoArr.meta.band_meta['wavelength'], dtype=float).flatten()
         tile = self.geoArr[:10, :5, :]
         tile[0, 0, 1] = -9999  # pixel with one band nodata
         tile[0, 1, 2] = -9999  # pixel with one band nodata